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Validation of the Data-Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA) among Hungarian In-Service Teachers

Title: Validation of the Data-Driven Decision-Making Efficacy and Anxiety Inventory (3D-MEA) among Hungarian In-Service Teachers
Language: English
Authors: Edmond Sebestyén (ORCID 0000-0002-6680-981X); Edit Tóth (ORCID 0000-0001-9281-5730)
Source: European Journal of Education. 2026 61(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 13
Publication Date: 2026
Document Type: Journal Articles; Reports - Research
Descriptors: Foreign Countries; Decision Making; Self Concept Measures; Self Efficacy; Data Use; Media Adaptation; Translation; Hungarian; Questionnaires; Teachers; Anxiety; Content Validity
Geographic Terms: Hungary
DOI: 10.1111/ejed.70346
ISSN: 0141-8211; 1465-3435
Abstract: The essence of data-driven decision-making (DDDM) is high efficacy and low perceived anxiety; therefore, numerous studies have focused on exploring these characteristics in various educational contexts. This study examined structural validity, the efficacy and anxiety characteristics of the 3D-MEA in the Hungarian context. The significance of our research lies in the fact that the questionnaire has not been examined in a non-US context. The paper focused on Hungarian teachers (N = 275), whose online responses led to the conclusion that the five-factor model of the questionnaire fits well with the Hungarian language and environment. Similar to international measurements, the Anxiety and Identification subscales produced higher values than those of the Technology, Interpretation and Application subscales. The results indicated that the questionnaire originally used in the Anglo-Saxon culture can be translated into the Hungarian language and educational environment. This research can lay the groundwork for further international comparative studies on 3D-MEA.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497727
Database: ERIC